Apparatus and method for event detection and duration determination

ABSTRACT

An asset class type of a new asset is predicted or determined based upon an evaluation of time series data from the new asset. A predicted asset type is used to identify sensors of the new asset to use for data collection. Using the readings of selected sensors from the new asset, states of the new asset are obtained. The duration at least one of these states of the new asset is determined. This information can be subsequently used to optimize the performance of the new asset.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Application62/531,036, entitled “Apparatus and Method for Event Detection andDuration Determination,” filed Jul. 11, 2017, which is incorporatedherein by reference in its entirety.

BACKGROUND OF THE INVENTION Field of the Invention

The subject matter disclosed herein generally relates to the detectionof events and measurement of the duration of these events that areassociated with the operation of industrial machines.

Brief Description of the Related Art

Various types of industrial machines or assets are used to performvarious manufacturing (or other) operations and tasks. For instance,some machines are used to create and finish parts associated with windturbines. Other machines are used to create mechanical parts orcomponents utilized by vehicles. Still other machines are used toproduce electrical parts (e.g., resistors, capacitors, and inductors tomention a few examples). Industrial machines often operate together inplants or factories. In other examples, multiple wind turbines may beorganized into wind farms, and machines may be organized as an assemblyline. In yet other examples, an elevator moves people or cargo fromfloor-to-floor in a building. In still other examples, vehicles (e.g.,cars, trucks, airplanes, or ships) are considered to be industrialmachines and perform various operations.

Industrial machines typically operate according to a series of events(or operate within and transition between different states). Forexample, the opening and closing of automated doors (e.g., as seen insupermarkets), the asset moving in vertical or horizontal direction, orthe state asset either in motion or at rest are all examples of eventsor states.

BRIEF DESCRIPTION OF THE INVENTION

The present invention is directed to automatically detect events andmeasure the duration of events for industrial assets. These approachesdiscover hidden system behavior without human supervision. Theapproaches identify the events or states of an industrial asset and thetime spent in the identified events or states.

Advantageously, no human supervision or training data is required tolearn or identify the system states. The approaches are applicable tomany industrial domains such as aviation, transportation and emergingverticals (e.g. transportation or aviation) to mention a few examples.The approaches are noise robust, that is, not susceptible to degradationdue to noise. Since the approaches are fully automated, no training orground truth is required, which lowers the cost of the systemsignificantly.

In many of these embodiments, multiple sensor measurements are utilizedto observe and measure activity and then the detection of activity isutilized to identify whether the asset is operating or not operating.The approaches may subsequently utilize asset operational modes todiscover, categorize, and model asset types as mathematical models.

The models are then used to identify the type of the asset for newassets that are coming on-line or joining existing assets (e.g., a newasset joining the fleet). Once the asset type is identified for the newasset, the asset type is used to identify relevant systemcharacteristics (e.g., identify sensors to collect relevant data) to aidin event detection. Modeling approaches are then used to classify theoperating status or states of the new asset, and to measure the time thenew asset spends in any given event or state. This information can besubsequently used to optimize the performance of the new asset (e.g.,re-program the asset or perform maintenance on the asset).

In others of these embodiments, an activity detection threshold indetermined and used to detect whether an activity has occurred in timeseries data obtained from an industrial asset. Time data statistics areobtained from the time series data, and the statistics are correlatedwith an asset class type. A model that maps measurements or determinedstatistics to asset class types is created.

An asset class type of a new asset is predicted or determined based uponan evaluation of time series data from the new asset. The predictedasset type is then used to identify sensors of the new asset to use fordata collection. Using the readings of selected sensors from the newasset (or statistics based upon these readings), states of the new assetare obtained. The duration of at least one of these states of the newasset is determined. This information can be subsequently used tooptimize the performance of the new asset.

In others of these embodiments, a model is stored in a computingenvironment. The model describes and predicts behavior of a new asset ormachine that is to be added to a group of currently operating assets ormachines. First time series data is sensed at the new machine with aplurality of sensors.

The type of the new asset or machine is determined based at least inpart upon a comparison of the model with the first time series data.Based upon the determined type, one or more of the plurality of sensorsare selected to obtain additional data. The selected ones of theplurality of sensors sense second time series data from the new machine.

One or more of an event, a state, or an event duration are determined atthe new machine based upon an analysis of the second time series data.An action that improves performance of the new machine is determined andis responsively based upon an evaluation of one or more of thedetermined event, state, or event duration.

In aspects, the model is built previous to sensing the first time seriesdata, the model being built based upon detected patterns in sensed thirdtime series data. In other aspects, determining an event utilizes asliding time window that is applied to the first time series data. Instill other aspects, the action is a maintenance action. In otherexamples, the action is the creation of an electrical control signal,and the electrical signal is transmitted to the new machine to controlan aspect of the operation of the new machine.

In examples, the one or more sensors are configured to sense speed,chemical composition of emissions, altitude, or gas pressure. In otherexamples, a Hidden Markov Model (HMM) is used to filter the events basedupon knowledge of a domain in which the new machine is operating.

In others of these embodiments, a system includes a network, a currentlyoperating machine that is coupled to the network, and a new machine thatis to be added with the currently operating machine. The new machine hasa plurality of sensors that sense first time series data at the newmachine. The computing environment is configured to store a model thatdescribes and predicts behavior of the new machine.

A control circuit is coupled to the network and is configured todetermine a type of the new machine based at least in part upon acomparison of the model with the first time series data. The controlcircuit is further configured to, based upon the determined type, selectone or more of the plurality of sensors to obtain additional data. Theselected ones of the plurality of sensors sense second time series datafrom the new machine. The control circuit is further configured todetermine one or more of an event, a state, or an event duration at thenew machine based upon an analysis of the second time series data, andto responsively determine an action that improves performance of the newmachine based upon an evaluation of one or more of the determined event,state, or event duration.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the disclosure, reference should bemade to the following detailed description and accompanying drawingswherein:

FIG. 1 comprises a block diagram of a system for determining activityand determining the duration of activity according to variousembodiments of the present invention;

FIG. 2 comprises a flow chart of an approach determining an event typeand determining the duration of activity according to variousembodiments of the present invention;

FIG. 3 comprises a flowchart that describes an approach for determiningan activity threshold according to various embodiments of the presentinvention;

FIG. 4A comprises a block diagram of data utilized in the flowcharts ofFIGS. 4B and 4C according to various embodiments of the presentinvention;

FIGS. 4B and 4C comprise flowcharts of an approach of determining amodel, and then using the model to determine an asset type according tovarious embodiments of the present invention;

FIG. 4D is a block diagram of data utilized in the flowcharts of FIGS.4B and 4C according to various embodiments of the present invention;

FIG. 5 comprises a flowchart showing an approach for the determinationof events or states of the asset or the new asset according to variousembodiments of the present invention;

FIG. 6 comprises a flowchart showing an approach for determining anevent type and duration according to various embodiments of the presentinvention;

FIG. 7 comprises a flowchart showing an approach for performing adiscretization step according to various embodiments of the presentinvention.

Skilled artisans will appreciate that elements in the figures areillustrated for simplicity and clarity. It will further be appreciatedthat certain actions and/or steps may be described or depicted in aparticular order of occurrence while those skilled in the art willunderstand that such specificity with respect to sequence is notactually required. It will also be understood that the terms andexpressions used herein have the ordinary meaning as is accorded to suchterms and expressions with respect to their corresponding respectiveareas of inquiry and study except where specific meanings have otherwisebeen set forth herein.

DETAILED DESCRIPTION OF THE INVENTION

In the present approaches, events (or states) occurring at industrialmachines are determined, and the duration of these events (or states) isdetermined. Advantageously, no human supervision or training data isrequired to learn or identify the states of the machine, or the durationof these states.

Referring now to FIG. 1, a system 100 for event detection and durationdetermination includes industrial machines 102, sensors 104, a network106, a control circuit 108, and a database 110.

The industrial machines (or assets) 102 may be any type of machine suchas windmills, boilers, reactors, elevators, computers, or vehicles tomention a few examples. Although the machines described herein arereferred to as industrial machines, it will be understood that themachines can be any type of device or asset whether operating in afactory environment, an industrial environment, a transportationenvironment, an office environment, or any other type of environment.

The sensors 104 are any type of apparatus that obtains or measures anytype of characteristic. For example, the sensors 104 may measurehumidity, temperature, speed, position, altitude, movement (e.g., up ordown, right or left) to mention a few examples.

The network 106 is any type of communication network or combination ofnetworks. For example, the network 106 may be disposed at the cloud. Inother examples, the network may include the internet. Other examples ofnetworks and combinations of networks are possible.

Transceivers or edge devices 105 allow the sensors 104 to communicatewith the network. In one example, the transceivers receive sensed ormeasured time series data 109 from the sensors 104, convert the datainto a form appropriate for network transmission, and then transmit thetime series data over the network 106. The data may be organized as datamatrices before the data is sent (with sensors being columns in thematrix, and rows representing different times, e.g., the first row beingtime t1, the second row being time t2, and so forth).

The control circuit 108 receives time series data from the sensors 106and performs various operations on this data. It will be appreciatedthat as used herein the term “control circuit” refers broadly to anymicrocontroller, computer, or processor-based device with processor,memory, and programmable input/output peripherals, which is generallydesigned to govern the operation of other components and devices. It isfurther understood to include common accompanying accessory devices,including memory, transceivers for communication with other componentsand devices, etc. These architectural options are well known andunderstood in the art and require no further description here. Thecontrol circuit 108 may be configured (for example, by usingcorresponding programming stored in a memory as will be well understoodby those skilled in the art) to carry out one or more of the steps,actions, and/or functions described herein.

The database 110 is any type of data storage device. The database 110and the control circuit 108 are shown in FIG. 1 as being located at thecloud. However, it will be appreciated that the control circuit 108 andthe database 110 may also be located remotely at the machines 102.

In one example of the operation of the system of FIG. 1, an activitydetection threshold is determined and used to determine whether anactivity has occurred in sensed or time series data 109 obtained fromthe industrial machines 102. Time data statistics are obtained from thedata 109, and the statistics are correlated with an asset class type(e.g., elevator, elevator door, or motor type) by the control circuit108. A model 111 that maps determined statistics to asset class types iscreated and, in some examples, is stored in a memory 120 in the controlcircuit 108. The memory may be a random access memory (RAM) in oneexample. In other examples, the model 111 is stored in a memory (e.g., aRAM) at one of the transceivers or edge devices 105.

A new machine 103 is to be added to the machines 102. An asset classtype of the new machine 103 is determined or predicted based upon anevaluation of time series data 115 from the new machine 103. Thepredicted model type is used by the control circuit 108 to identifywhich of the sensors 113 of the new machine 103 to use for dataanalysis. Using the readings of selected sensors from the new machine103 (or statistics, e.g., mean values, associated with this data),states of the new machine 103 are obtained or determined by the controlcircuit 108. The duration at least one of these states of the newmachine 103 is determined by the control circuit 108. This informationcan be subsequently used to optimize the performance of the new machine103. For example, the control circuit 108 may generate electronicsignals sending messages to users. Electronic signals may be sent to themachine 103 that may re-configure or re-program aspects of the machine103.

In yet another example, the transceiver 105 is an edge device and thenetwork 106 is the cloud. As shown, the sensors 104 are connected toindustrial machines 102. The sensors 104 gather data on these machinessuch as humidity, temperature, speed position to name a few examples.The data collected by the sensors 104 are then sent to the edge device105. In aspects, the edge device 105 is an electronic computing devicethat is equipped with a processor and memory. The edge device 105 isoften connected to the cloud 106 via another network (i.e. an ethernet,Wi-Fi, or cellular network).

In other aspects, the models 111 (e.g., machine learning models) arehosted by the edge devices 105, and the models 111 reside in the memoryof the edge devices 105. The data 109 collected from the sensors 104 isthen processed by the model 111 and sent to the cloud 106. The data 109can either reside for a certain time at an edge device 105 until it issent to cloud 106 or off-loaded at a certain time. For example, sensors104 that are connected to turbines may report an increase in thetemperature. When the temperature reaches a certain threshold asdetermined by the model 111, an alert is sent to the cloud 106 to takeimmediate action. In another example, the take-off and landinginformation along with the altitude change of an aircraft are recordedby the sensors 104 and processed by the model 111 on the aircraft. Theanalysis done by the model 111 is saved in memory during the flight andoff-loaded when the aircraft lands and reaches a terminal.

In still another example, the sensors 104 are connected to the machine102 and send data 109 to the cloud 106 over another network (i.e.ethernet, Wi-Fi, or cellular network). The sensor data 109 is thenstored in a database 110. The machine learning models 111 created for acertain task, reside in the memory 120 of the control circuit 108. Inaspects, the control circuit 108 (or cloud computing approaches) iscomposed of multi-processors and larger memory than the edge device 105.The machine learning models 111 access the sensor data 109 from thedatabase 110 and output results.

In yet another example, the sensors 104 are connected to the machine 102and send data to the cloud 106. The data is then stored in the database110. The models 111 in the control circuit 108 access the sensor data109 from the database 110. A model created for a certain task, trainsitself on this data. Once, the model is sufficiently robust for acertain task, the model is sent to the edge device 105 via the network106 where it processes incoming data from the sensors 104.

It will be appreciated that the output of the models 111, which areactionable insights, can be sent to dashboards where the data isdisplayed for technicians. The dashboards help the technicians to takeactions on what they see. It can also be seen that certain models workin-situ (i.e., on the edge device 105) while others work remotely (i.e.in the cloud 106).

Referring now to FIG. 2, one example of an approach for event detectionand duration determination is described. Sensors 201 obtain time seriesdata from industrial machines or assets. At step 202, data from sensors201 is obtained, an activity threshold is determined from the timeseries data, and a determination is made as to whether activity isdetected in the time series data obtained from the sensors 201.

At step 204, asset type identification occurs. In this step, adetermination is made as to the asset type(s) in the time series data.At step 206, a model is created. In aspects, the model maps asset typesto data patterns (or statistics) from particular assets. Once a model isdetermined, data from new assets can be compared to the model and adetermination of the asset type of the new asset can be made.

At step 208, asset event inference occurs. In this step, a new asset isadded to the group of existing assets, the model is used to determine anasset type, and then time series data from the new model is evaluated todetermine an event or states of the new asset. A determination as to thenumber of events and states over time can be made. The duration of theseevents and states may also be determined. This information can besubsequently used to optimize the performance of the new asset. Forexample, messages may be sent to users to perform maintenance on themachine. Electronic signals may be sent to the machine that mayautomatically re-configure or re-program aspects of the machine. Otherexamples are possible.

FIG. 3 is an example of an approach for determining an activitythreshold. At step 302, sensor data is received from a machine in theform of a matrix. The matrix may include columns and rows. The rows mayrepresent different times, and the columns are different sensors, withdata from a sensor at a particular time located at each matrix location.

At step 304 a time zone (range of times) with perceived or known lowactivity is selected. By “low activity,” it is meant a usage parameter(e.g., how many times the asset is used) falls below or is expected tofall below a threshold value. In examples, a time when there is littlehuman activity (so that there is no activity with the asset) isselected. The low activity time may be different for different types ofassets. The time zone may be 2:00-5:00 am in one example.

At step 306, a random window position (e.g., 2:00-2:30 am) is madewithin the overall time window. At step 308, this window position isdivided into still smaller segments (e.g., five minute segments, e.g.,2:00-2:05 am). At step 310, statistics are computed for a five-minutesegment. Descriptive statistics such as standard deviation, mean, ordynamic range are computed for the five-minute segment. Then, thisprocess is repeated until statistics for all five-minute segments withinthe 2:00-2:30 am window are computed.

At step 312, the median value in the time window (e.g., 2:00-2:30 am) isdetermined. The median value is selected as it is robust so as not to beaffected by any outliers that may exist. Then, the process returns tostep 306, to analyze the next window (e.g., 2:30-3:00 am).

When all windows within the 2:00-5:00 am time period are processed, themode within all the windows, as a majority rule, is determined (i.e., avalue that is most common) at step 314. This value becomes the initialactivity threshold for the given sensor and is stored in theconfiguration file on the system. That is, when a value of new data isat or above the activity threshold, then activity exists. When the valueof new data is below this value, no activity exists. The configurationfile is then used to store and update the activity threshold value on aregular basis such that the whole process is made dynamic. Any updatingprocedure can be used to orchestrate this process. This decision is leftto the user.

Referring now to FIGS. 4A, 4B, 4C, and 4D (collectively referred toherein as “FIG. 4,”) an approach for asset identification and mapping(creation of a mathematical model representation of assetcharacteristics) is described. The approach described in FIG. 4 resultsin the creation of a model at step 422.

One purpose of the approach of FIG. 4 is to identify patterns andclasses that define attributes of a given asset where systemcharacteristics are available via domain experts and capture thatinformation into a mathematical model representation. The model is thenused to infer characteristic attributes/classes of new assets where suchsystem information is unavailable (e.g. third party or legacy systems).The approach of FIG. 4 then uses the asset class type to select theappropriate inputs and set model parameters in order to detect assetevents and provide high accuracy for event inference.

At step 402, a sensor data matrix is received. At step 404, it isdetermined whether activity exists in the received data. This is basedon the premise that asset signatures are distinguishable only when thereis activity occurring and is implemented to provide higher accuracy. Anapproach may be utilized to determine whether activity exists. If noactivity is recognized in the received data, the data segment beinganalyzed is discarded at step 406.

On the other hand, if activity is recognized, then step 408 is executed.At step 408, the sensor data matrix is split into smaller time windows.The size of these window may be determined by a user or supervisor.

At step 410, for each time window, sensor(s) time domain statistics aredetermined from the sensor data matrix, and a statistics matrix 493 iscreated. In examples, the statistics in the matrix may include the mean,the median, or data skewness. Other examples are possible. Thestatistics matrix 493 is produced to include this determinedinformation. The rows of the statistics matrix represent a time and amachine (e.g., machines E1 or E2). The columns in this matrix areparticular statistics present for a sensor at a time.

At step 412, a Fast Fourier Transform (FFT) is applied to the data totransform the data from the time to frequency domain. The purpose ofthis step is to add system characteristics that are present in thefrequency domain. Asset classes can be separated based upon frequencycharacteristics.

At step 414, for each time window, inter sensor covariance is generated.In this step, inter sensor time domain and energy features arecalculated in order to infer time and state-based interactions withinthe system. For example, a time series of correlation coefficients maybe generated for all possible sensor combinations available to thedesigner. In one specific example, correlation coefficients betweenhumidity and gas pressure may be calculated.

If domain specific information is available and data can be categorized,a pre-programmed reference matrix 494 is created, for example, by asystem supervisor. In aspects, the matrix 494 has rows representing amachine that is mapped to an asset type (e.g., E1 is a machine that ismapped cargo airplane, E2 is a machine that is mapped to passengerairplane), and columns that are statistics. The matrix 494 may bepre-programmed into the system, for example, by a supervisor.

Prior to step 420, the pre-programmed reference matrix 494 and thestatistics matrix 493 produced at step 415 are merged together as inputs(495) to the supervised modeling step 420 (represented by an equation:(y=Fn(x1, x2, . . . Xn)) where y is the asset type, x1 is a firststatistic, x2 is a second statistic, and xn is an nth statistic). Themodel here is a mathematical transformation that provides a mapping fromthe domain of the asset sensor data to an asset class type based onsupervision.

If domain expert supervision or labeled categories of the asset or itselements are unavailable, the sensor feature data produced from step 415advances to step 418.

At step 422, a model is selected to detect the type of asset and theasset's signals using one of the following approaches: a) a supervisedlearning based asset class identification approach, when informationfrom domain or expert is available, at steps 420, 421, orb) anunsupervised learning based asset class identification approach, whendomain specific details are unavailable or data is anonymous orunlabeled, at steps 418, 419.

The supervised learning based asset class identification approach (steps420, 421) is now described. Data that has been identified as belongingto a group based on a domain expert is referred to as training data(e.g. matrix 442). The training data consists of a set of trainingexamples (e.g. a domain expert may inform some types may contain highforces at specified frequencies). The combination of the engine typewith the force magnitudes at various spectra becomes a training samplefor that engine type). In supervised learning, each example is a pairconsisting of an input object (typically a vector or matrix of sensorfeatures) and a desired output value (also called the supervisorysignal). In this case, it would be the asset class or attribute. Asupervised learning algorithm analyzes the training data and produces aninferred function, which can be used for mapping new examples. In thisinstance, the supervised algorithm is selected so as to provide the bestcross-validation metrics. Some of the candidate algorithms includeK-nearest neighbor approaches, Random Forest approaches, andsupport-vector machines (SVM) approaches. These can be used with thetraining data to identify the approach that provides the best mapping tothe asset class based on optimizing the cross-validation metrics.

Additional supervised training methods may be considered by a user basedon the characteristics of the data. For instance, cross-validation thatinvolves partitioning data into subsets, performing the analysis on onesubset (called the training set), and validating the analysis on theother subset (called the validation set or testing set) can be utilized.

The method that best determines the class types across all the subsetsis considered to optimize the cross-validation score. This step isrequired as the input data dictates the model that best fits the classtypes within the data. For instance, an SVM may best explain the dataclasses based on CT images, whereas the K-Nearest Neighbors may bestexplain an engine propulsion mode.

The un-supervised learning based asset class identification approach(steps 418, 419) is now described. When domain expert supervision isunavailable and the relationship between the underlying categories ofassets to the data are unknown, i.e. the examples provided to thelearner are unlabeled, these approaches may be used.

Several approaches exist for selecting a mathematical model that bestrepresents the hidden asset classes in the data using unsupervisedlearning methods. Such approaches include but are not limited toK-Means, Spectral clustering, Gaussian Mixtures, and PrincipalComponents Analysis based hierarchical clustering. There may be othermethods that a user skilled in the art could apply based on the datacharacteristics.

Once the various approaches are applied, the approach that results inthe highest intra-asset class similarity and lowest inter-asset classsimilarity is then chosen as the candidate unsupervised model for thegiven application. It will be appreciated that other metrics may also beused by a user skilled in the art and may include normalized mutualinformation and purity, Bayesian or Aikake information criteria.

The model is a transform that maps statistics to an asset type developedusing the approaches identified in FIG. 4. In the present example,statistics of 1 and 2 map to the type cargo airplane (CA). Statistics of3 and 4 map to the type cargo airplane Statistics of 10 and 15 map tothe type passenger airplane (PA). Thus, and in this example, y (thetype) is defined by sensor readings (x1, x2).

Referring now to FIG. 5, and now having a label for an asset (e.g.,produced by the algorithm of FIG. 4) and new or additional data from theasset (or a new asset), a determination is made as to events or statesof the asset or new asset. At step 502, the new or additional data inthe form of a data matrix is received. At step 504, the asset type label(e.g., determined by the approach described in FIG. 4) is obtained. Forexample, the asset type CA is obtained.

At step 506, the sensors that are to be used for the asset type aredetermined. For example, asset type CA may have a predetermined list ofsensors associated with this type. A look-up table may be used torepresent this mapping.

At step 508, based on the asset classes identified and down-selection ofsensors used for instance for asset class X (e.g., time domain) sensorfeatures are obtained. At step 510, for an asset of class Y (e.g.,energy sensor) features are obtained. At step 512, if the asset class isZ (e.g., frequency domain) sensor features are obtained. By “features,”it is meant physical measures of an asset (or element of the asset)event, performance, operation, movement, or state. For example, if theasset is an elevator, the feature may be a current value or a forcevalue. It will be appreciated that these types of values or parametersare examples only, and that other examples are possible.

At step 514, continuous sensor feature values are converted todiscretized sequences. K-means clustering as known to those skilled inthe art may be used to obtain these flags. For a “door opening” event, a0 may be chosen. For a “door closing” event, a 1 may be assigned. Thedata obtained is then mapped according to these event flags as describedbelow.

One example of an implementation of step 514 is described with respectto FIG. 7 elsewhere herein. The sensor feature values are inputs to theK-Means clustering algorithm, that discretizes the data to apre-determined set of bins. Sequence counting is then performed to getrun lengths of each sequence. For instance, the sequence(000000001111111111) may yield the following sequence value=0, sequencelength=8, followed by sequence value=1, sequence length=10. Filtering isperformed to correct or remove run lengths that are anomalous too smallor too long based on domain expertise. For example, in the aboveexample, if there was an error (000000001111001111), where (00) areinserted due to an error, they would be rectified by the filteringalgorithm. The original correct sequence is reconstructed to(000000001111111111) and passed on to the next step which is the eventinference using hidden Markov modeling approaches as known to thoseskilled in the art.

At step 516, HMM latent state detection is performed. With this step, amulti-latent states Hidden Markov Model (HMM) is applied on thenormalized window signal input. This HMM tries to classify the data intoevent signals, non-event signals, and noise while taking the timedependency of data points into account. The identified noise is thendiscarded and events/non-events are flagged.

At step 518, domain knowledge based HMM filter for binary events isperformed. With this step, another HMM filter is applied. One purpose ofthis filter is to use domain knowledge to filter out events that areunlikely to happen, such as an airplane takeoff is unlikely take morethan some amount of time. Such domain knowledge can be coded into thetransition matrix and emission matrix parameters of HMM. This stepgenerates a binary flagged signal indicating an event (e.g., an airplanetaking-off/landing) and a non-event (e.g., airplane cruising, or idle).In this example, it is assumed that airplane cruising and idle havesimilar patterns because the airplane is not performing drastic actionsin either situation.

At step 520, left-to-right HMM modeling asset true status is performed.With this step, a left-to-right HMM is applied to detect the actualstates of the asset. This HMM is parameterized to reflect the assetactual behavior, such as an airplane going from idle to takeoff, tocruise, to landing, and repeating the process. This MINI will label thesignal based on this logic, and the output of this model is recorded asthe final outputs of detected actual events.

In this example (and as shown in FIG. 6), the events are generated alongan x-axis. The x-axis is a time axis with arbitrary time units.Consequently, and examination of the time axis with respect to an eventtype yields the time duration of that event. After the completion ofstep 520, the type and duration of the event has been determined.

FIG. 6 is an approach for determining asset events and durations. Thisapproach can be widely applied to different assets (e.g., an airplane)that are monitored by sensors with time series monitoring data. Thisapproach will be able to robustly detect events (e.g., airplane takeoff,cruising, landing, and idle to mention a few examples) of this asset. Ingeneral, as shown, noisy and non-stable input signals 600 are generatedby the asset as the input to the approach of FIG. 6. Step 605 applies amodel to detect the events (e.g., asset drastic actions, such asairplane takeoff and landing) and noise. Step 607 then filters throughthe events by a domain knowledge based Hidden Markov Model (HMM) (e.g.,landing has to happen after airplane takeoff, landing has to be at least10 minutes) to further clean up the events. Step 609 uses an event orderbased HMM to detect the actual asset states (e.g., airplane takeoff,cruise, or landing, idle).

At step 603, overlapping windows are implemented. Streaming data arebroken into a series of overlapped windows. This windowing method allowmodel to be applied constantly on the streaming data to generate liveevent detection results, and the overlapping confirms that events occursat the boundaries of a window is still fully captured. Morespecifically, the event could be on the boundaries of both event.Breaking a short-lived event may lead to errors in identificationbecause their durations are further shortened. The overlapped windowensures that the full event is captured in at least 1 of the windows. AZ-score normalization is applied afterwards.

At step 605, a multi-latent states Hidden Markov Model (HMM) is appliedon the normalized window signal input. This HMM tries to classify thedata into event signals, non-event signals, and noise while taking thetime dependency of data points into account. The identified noise isthen discarded and events/non-events are flagged.

At step 607, another HMM filter is applied. The main purpose of thisfilter is to use domain knowledge to filter out events that are unlikelyto happen, such as that an airplane takeoff is unlikely to take above apredetermined amount of time. Such domain knowledge can be coded intothe transition matrix and emission matrix parameters of HMM. After step607 has executed, a binary flagged signal indicating an event (e.g.,airplane taking-off/landing) and non-event (e.g., airplane cruising,idle) is generated. In this example, it is assumed that airplanecruising and idle has similar patterns in this specific example becauseairplane is not performing drastic actions in either situation.

At step 609, a left-to-right HMM is applied to detect the actual statesof the asset. This HMM is parameterized to reflect the asset actualbehavior, such as an airplane will go from idle to takeoff, to cruise,to landing, and the process is repeated. This HMM will label the signalbased on this logic, and the output of this model is recorded as thefinal outputs of detected actual events.

In this example, the events are generated along an x-axis. The x-axis isa time axis with arbitrary time units. Consequently, and examination ofthe time axis with respect to an event type yields the time duration ofthat event. At the conclusion of step 609, the type and duration of theevent has been determined.

Referring now to FIG. 7, one example of the implementation of featurediscretization (step 514 of FIG. 5) is described. At step 702, discreteevent flags are generated, for example using K-means clustering. Morespecifically, a K-Means clustering algorithm discretizes the data to apre-determined set of bins (e.g., memory locations).

At step 704, state sequence generation using run length encoding isperformed. More specifically, sequence counting is performed to get runlengths of each sequence. For instance (000000001111111111) may yieldthe following sequence value=0, sequence length=8, followed by sequencevalue=1, sequence length=10.

At step 706, outlier segments are filtered. Filtering is performed tocorrect or remove run lengths that are anomalous too small or too longbased on domain expertise. For example, if there was an error(000000001111001111), where (00) was inserted due to an error, thiserror would be rectified by the filtering algorithm.

At step 708, inverse run length encoding is performed. In this step, theoriginal correct sequence (000000001111111111) is reconstructed andpassed on to the next step which is the event inference using hiddenMarkov modeling.

In other aspects, a neural network with multiple data steams (some withdata indicating physical movement of an elevator, some not indictingphysical movement of an elevator) is trained. When the trained neuralnetwork obtains another data stream, it is able to determine if the datastream contains data indicating physical movement. The data may beobtained from sensors such as accelerometers.

More specifically, a feedforward neural network maps an input featurevector x, which contains accelerometer values of a data stream, onto atarget variable, which can be interpreted as the probability that thedata stream contains a trip. More specifically, multiple data streamsare collected and this data indicates elevator movement (or nomovement). In examples, the amount of data is reduced by computing themean of equidistant time intervals (e.g., 18 intervals) and this obtainsthe input feature vector x. The feature vector can then be normalizedinto an interval (e.g., the interval [−1, 1]). Once normalized, it canbe determined if the data exceeds a threshold, which indicates movement.

It will be appreciated by those skilled in the art that modifications tothe foregoing embodiments may be made in various aspects. Othervariations clearly would also work, and are within the scope and spiritof the invention. It is deemed that the spirit and scope of theinvention encompasses such modifications and alterations to theembodiments herein as would be apparent to one of ordinary skill in theart and familiar with the teachings of the present application.

What is claimed is:
 1. A method, the method comprising: storing a modelin a memory, the model describing and predicting behavior of a newmachine that is to be added to a group of currently operating machines;sensing first time series data at the new machine with a plurality ofsensors; determining an activity value for the first time series dataand discarding the first time series data when the activity value doesnot reach an activity threshold; when the activity value reaches theactivity threshold, determining a type of the new machine based at leastin part upon a comparison of the model with the first time series data;mapping the determined type to one or more sensors from the plurality ofsensors, only the one or more sensors from the plurality of sensorssensing second time series data from the new machine; determining one ormore of an event, a state, or an event duration at the new machine basedupon an analysis of the second time series data; and responsivelydetermining an action that improves performance of the new machine basedupon an evaluation of one or more of the determined event, state, orevent duration; wherein the electrical signal is transmitted to the newmachine to control an aspect of the operation of the new machine, andthe electrical control signal is applied to the new machine.
 2. Themethod of claim 1, wherein the model is built previous to sensing thefirst time series data, the model being built based upon detectedpatterns in sensed third time series data.
 3. The method of claim 1,wherein determining an event utilizes a sliding time window that isapplied to the first time series data.
 4. The method of claim 1, whereinthe action is a maintenance action.
 5. The method of claim 1, whereinthe one or more sensors are configured to sense speed, electricalcurrent, movement, pressure or temperature.
 6. The method of claim 1,wherein a Hidden Markov Model (HMM) is used to filter the events basedupon knowledge of a domain in which the new machine is operating.
 7. Asystem, the system comprising: a network; a currently-operating machinethat is coupled to the network; a new machine that is to be added withthe currently operating machine, the new machine having a plurality ofsensors that sense first time series data at the new machine; a controlcircuit including a memory that stores the model that describes andpredicts behavior of the new machine, the control circuit being coupledto the network, the control circuit being configured to determine anactivity value for the first time series data and discarding the firsttime series data when the activity value does not reach an activitythreshold, the control circuit being configured to, when the activityvalue reaches the activity threshold, determine a type of the newmachine based at least in part upon a comparison of the model with thefirst time series data, the control circuit further configured to mapthe determined type to one or more sensors from the plurality ofsensors, only the one or more sensors from the plurality of sensorssensing second time series data from the new machine, the controlcircuit being further configured to determine one or more of an event, astate, or an event duration at the new machine based upon an analysis ofthe second time series data; and responsively determine an action thatimproves performance of the new machine based upon an evaluation of oneor more of the determined event, state, or event duration; wherein theelectrical signal is transmitted to the new machine to control an aspectof the operation of the new machine, and the electrical control signalis applied to the new machine.
 8. The system of claim 7, wherein thecontrol circuit builds the model previous to sensing the first timeseries data, the model being built based upon detected patterns insensed third time series data.
 9. The system of claim 7, wherein thecontrol circuit determines an event by utilizing a sliding time windowthat is applied to the first time series data.
 10. The system of claim7, wherein the action is a maintenance action.
 11. The system of claim7, wherein the one or more sensors are configured to sense speed,electrical current, movement, pressure or temperature.
 12. The system ofclaim 7, wherein the control circuit utilizes a Hidden Markov Model(HMM) is used to filter the events based upon knowledge of a domain inwhich the new machine is operating.